Such extension is possible because the DONNA accuracy predictor generalizes to previously unseen architectures, without having to extend the Architecture Library. This is illustrated in Fig. REF (left), showing the DONNA predictor achieves good quality, in line with the original test set, on a ShiftNet-based test set of architectures.
Figure REF (left) further illustrates this extrapolation works by showing the confirmed results of a search for the ShiftNet space. Note how the trendline predicts the performance of full Pareto optimal ShiftNets even though the predictor is created without any ShiftNet data. Here, ShiftNets are our implementation, with learned shifts per group of 32 channels as depthwise-separable replacement. These generalization capabilities are obtained because the predictor only uses quality metrics as an input without requiring any structural information about the replacement block.
This feature is a major advantage of DONNA compared to OFA [1]} and other methods where the predictor cannot automatically generalize to completely different layer-types, or to blocks of the same layer-type with parameters (expansion rate, kernel size, depth, ...) outside of the original search space. Appendix  illustrates such extension can also be used to model accuracy of lower precision quantized networks.
